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We propose the task of forecasting characteristic 3d poses: from a shortsequence observation of a person, predict a future 3d pose of that person in alikely action-defining, characteristic pose -- for instance, from observing aperson picking up an apple, predict the pose of the person eating the apple.Prior work on human motion prediction estimates future poses at fixed timeintervals. Although easy to define, this frame-by-frame formulation confoundstemporal and intentional aspects of human action. Instead, we define asemantically meaningful pose prediction task that decouples the predicted posefrom time, taking inspiration from goal-directed behavior. To predictcharacteristic poses, we propose a probabilistic approach that models thepossible multi-modality in the distribution of likely characteristic poses. Wethen sample future pose hypotheses from the predicted distribution in anautoregressive fashion to model dependencies between joints. To evaluate ourmethod, we construct a dataset of manually annotated characteristic 3d poses.Our experiments with this dataset suggest that our proposed probabilisticapproach outperforms state-of-the-art methods by 26% on average.

Forecasting Characteristic 3D Poses of Human Actions